Existing tools for classifying data streams can help reduce the complexity of certain tasks. As a particular example, tools exist for classifying a stream of network alarms based on the relative severity of the alarms, the cause of the alarms, or other alarm attributes. Such tools typically use expert-defined rules to automate classification of the individual alarms. However, such rule-based systems are relatively inflexible and generally do not adapt well to changing network conditions or other contingencies. Furthermore, improvements to existing rule-based systems typically require continuous expert involvement to refine the system rules offline.
Techniques also exist for visualizing streaming data and various classifications of the streaming data. For example, network alarms can be displayed in a tabular format including various alarm attributes on a graphical user interface. However, existing visualization techniques are frequently decoupled from the underlying classification systems. As one example, users may make real-time decisions to reclassify streaming data items using graphical user interfaces such as the aforementioned tabular formats. However, these real-time decisions typically are not incorporated into the underlying classification systems.